In recent years, enterprises have shifted much of their computing needs from enterprise owned and operated computer systems to cloud-computing providers. Cloud-computing providers charge enterprises to store and run their applications in a cloud-computing infrastructure and allow enterprises to purchase other computing services in much the same way utility customers purchase a service from a public utility. A cloud-computing infrastructure may be consolidated into a single data center or distributed geographically over numerous data centers, each data center composed of numerous racks of servers, switches, routers, and mass data-storage devices interconnected by local-area networks, wide-area networks, and wireless communications.
IT managers of cloud-computing infrastructures rely on cloud-computing-management systems to generate reliable and accurate information regarding any current abnormalities and identify bottlenecks associated with running each enterprise's applications, and promptly generate actionable recommendations to handle the abnormalities. In an effort to generate reliable and accurate information that may be used to identify current abnormalities, modem cloud-computing infrastructures now generate and store millions of different types of metrics over time that may be referred to as “big data.” Each metric may be a measure of a different aspect of running an enterprise's application in a cloud-computing infrastructure. For example, one metric may measure the number of users of an application, another metric may measure the response time of the application, while other metrics may each measure how much certain cloud-computing resources are used by the application. Abnormalities are typically identified when a metric violates a threshold. However, because of an ever increasing volume of metric data that is generated and stored over time, efforts to identify and isolate abnormalities in these large volumes of metric data is becoming increasingly more challenging. IT managers seek methods and systems to manage these increasing volumes of metric data.